Systems and methods for improved detection of network fraud events

ABSTRACT

A method and system for detecting fraudulent network events in a payment card network are provided. A plurality of scored payment card transaction authorization requests are received, originating from a plurality of merchants, and data structures for each of a plurality of merchant groups are generated. Each data structure sorts the scored authorization requests into fraud score range stripes. The data structures are parsed over a plurality of time periods, and at least one cumulative metric is calculated for each merchant group for each of the time periods in each fraud score range stripe. A plurality of ratio striping values is determined for each merchant group, and a set of feature inputs is generated using the ratio striping values. A second fraud detection model is applied to the scored authorization requests. Parameters of the second fraud detection model are configured to change based on the generated set of feature inputs.

BACKGROUND

This disclosure relates generally to fraud detection in a network and, more particularly, to computer systems and computer-based methods for early detection of a fraud attack on the network.

Payment processing networks process numerous payment card transactions every day through numerous merchants. Most of these payment card transactions are valid transactions. However, at least some of these payment card transactions are fraudulent. Payment card transaction processors, such as payment networks and issuing banks, may monitor payment card transactions for signs of fraudulent activity. At least some known fraud detection systems monitor payment card transactions one payment card transaction at a time to determine whether the payment card transaction is potentially fraudulent. Computer models used to monitor and detect fraud are static models, in that, once set, the models analyze the payment card transactions in the same way over time. The static models may not be able to detect low-level fraud attacks or changing tactics of the fraudulent activity.

BRIEF DESCRIPTION

In one embodiment, a computing system for detecting fraudulent network events in a payment card network includes a merchant profiling engine configured to receive a plurality of scored payment card transaction authorization requests originating from a plurality of merchants. Each of the scored payment card transaction authorization requests is associated with a respective preliminary fraud score. The merchant profiling engine is also configured to generate, for each of a plurality of merchant groups of the plurality of merchants, a respective data structure. The data structure sorts the scored payment card transaction authorization requests associated with the respective merchant group over a plurality of fraud score range stripes based on the corresponding preliminary fraud score. The merchant profiling engine is also configured to parse each of the data structures over a plurality of time periods. Each of the time periods extends back over a respective predetermined interval from a common starting point. The merchant profiling engine is further configured to calculate, for each merchant group for each of the time periods, at least one cumulative metric from the scored payment card transaction authorization requests associated with each of the fraud score range stripes. The merchant profiling engine is also configured to determine, for each merchant group, a plurality of ratio striping values. Each ratio striping value is a ratio of a first value of the at least one cumulative metric in a first of the fraud score range stripes from a first time period with respect to a second value of the at least one cumulative metric in the first fraud score range stripe from a second time period, wherein the second time period extends back farther in time than the first time period. The merchant profiling engine is further configured to generate a set of feature inputs using the determined plurality of ratio striping values. The computing device also includes a downstream fraud detection model communicatively coupled to the merchant profiling engine. The downstream fraud detection model is configured to apply a second fraud detection model to the scored payment card transaction authorization requests. Parameters of the second fraud detection model are configured to change based on the generated set of feature inputs.

In another embodiment, a computer-implemented merchant profiling method for detecting fraudulent network transactions in a payment card transaction network uses at least one computing device having at least one processor. The method includes receiving a plurality of scored payment card transaction authorization requests originating from a plurality of merchants. Each of the scored payment card transaction authorization requests is associated with a respective preliminary fraud score. The method also includes generating, for each of a plurality of merchant groups of the plurality of merchants, a respective data structure. The data structure sorts the scored payment card transaction authorization requests associated with the respective merchant group over a plurality of fraud score range stripes based on the corresponding preliminary fraud score. The method further includes parsing each of the data structures over a plurality of time periods. Each of the time periods extends back over a respective predetermined interval from a common starting point. The method additionally includes calculating, for each merchant group for each of the time periods, at least one cumulative metric from the scored payment card transaction authorization requests associated with each of the fraud score range stripes. The method further includes determining, for each merchant group, a plurality of ratio striping values. Each of the ratio striping values is a ratio of a first value of the at least one cumulative metric in a first of the fraud score range stripes from a first time period with respect to a second value of the at least one cumulative metric in the first fraud score range stripe during a second time period, wherein the second time period extends back farther in time than the first time period. The method also includes generating a set of feature inputs using the determined plurality of ratio striping values and applying a second fraud detection model to the scored payment card transaction authorization requests. Parameters of the second fraud detection model are configured to change based on the generated set of feature inputs.

In yet another embodiment, at least one non-transitory computer-readable storage media has computer-executable instructions embodied thereon. When executed by at least one processor, the computer-executable instructions cause the at least one processor to receive a plurality of scored payment card transaction authorization requests originating from a plurality of merchants. Each of the scored payment card transaction authorization requests is associated with a respective preliminary fraud score. The computer-executable instructions cause the at least one processor to generate, for each of a plurality of merchant groups of the plurality of merchants, a respective data structure. The data structure sorts the scored payment card transaction authorization requests associated with the respective merchant group over a plurality of fraud score range stripes based on the corresponding preliminary fraud score. The computer-executable instructions also cause the at least one processor to parse each of the data structures over a plurality of time periods. Each of the time periods extends back over a respective predetermined interval from a common starting point. The computer-executable instructions further cause the at least one processor to calculate, for each merchant group for each of the time periods, at least one cumulative metric from the scored payment card transaction authorization requests associated with each of the fraud score range stripes. The computer-executable instructions additionally cause the at least one processor to determine, for each merchant group, a plurality of ratio striping values. Each ratio striping value is a ratio of a first value of the at least one cumulative metric in a first of the fraud score range stripes from a first time period with respect to a second value of the at least one cumulative metric in the first fraud score range stripe from a second time period, wherein the second time period extends back farther in time than the first time period. The computer-executable instructions also cause the at least one processor to generate a set of feature inputs using the determined plurality of ratio striping values and apply a second fraud detection model to the scored payment card transaction authorization requests. Parameters of the second fraud detection model are configured to change based on the generated set of feature inputs.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1-8 show example embodiments of the methods and systems described herein.

FIG. 1 is a simplified block diagram of an example fraud analysis computing system for detecting fraudulent network events in a payment card interchange network in accordance with one example embodiment of the present disclosure.

FIG. 2 illustrates a graphical user interface generated by the computing system shown in FIG. 1.

FIG. 3 is a schematic diagram illustrating an example multi-party payment card industry system for enabling ordinary payment-by-card transactions in which merchants and card issuers do not necessarily have a one-to-one relationship.

FIG. 4 is a simplified block diagram of the fraud analysis computing system shown in FIG. 1 in communication with the payment interchange network shown in FIG. 3 in accordance with one embodiment of the present disclosure.

FIG. 5 illustrates an example configuration of a client system shown in FIG. 4.

FIG. 6 illustrates an example configuration of a server system shown in FIG. 4.

FIG. 7 shows an example configuration of the fraud analysis computing system shown in FIG. 1.

FIG. 8 is a flow diagram of a computer-implemented merchant profiling method for detecting fraudulent network transactions in a payment card transaction network.

DETAILED DESCRIPTION

Embodiments of the present disclosure describe a fraud detection computer device and method implemented using a computing system that is in communication with a fraud detection system and a data warehouse associated with a payment card network. The methods and systems described herein utilize multiple fraud detection models in series in real time. Initially, payment card transaction authorization requests are scored on a per transaction basis for a likelihood of the underlying payment card transaction being a fraudulent transaction. After the payment card transaction authorization requests have been individually scored, a merchant profiling engine processes the requests and produces additional data about groups of transaction authorization requests. The additional data is provided as an input to downstream fraud detection models. For example, the downstream fraud detection models include machine learning algorithms that are selectable while the fraud detection computer device is operating, and the additional data is used to generate feature inputs that alter operation of the machine learning algorithms.

The merchant profiling engine receives the scored payment card transaction authorization requests, or a representation of the scored payment card transaction authorization requests that do not have the full complement of data contained in payment card transaction authorization requests. For example, for personally identifiable information concerns, only a subset of the data in each payment card transaction authorization request may be transmitted to the merchant profiling engine. This might be important if the merchant profiling engine processing was performed by a third party service provider. In one embodiment, only the fraud score and timestamp data from each authorization request is necessary for the merchant profiling engine to give meaningful output data.

The process of the merchant profiling engine may be visualized on several graphs. Each graph shows data regarding payment card transaction authorization requests for a merchant, merchant bank, merchant category, or other grouping of merchants. The merchant, merchant bank, merchant category, or other grouping of merchants selected for analysis may be referred to as a merchant group (it should be understood that a particular “merchant group” may encompass only a single merchant). The graph has an x-axis graduated in units of time and a y-axis graduated in units of fraud score. The incoming scored payment card transaction authorization requests are displayed on the graph, metrics associated with the payment card transaction authorization requests in different zones of the graph are tracked, and ratios of the metrics are computed and processed to generate inputs into a downstream fraud detection model that refines the fraud analysis according to electronic models and/or machine learning algorithms. Within each zone, for example, the computing device tallies the number of payment card transaction authorization requests within the zone, determines the total value of the amounts (e.g., monetary amounts) of the payment card transaction authorization requests within the zone, and counts the number of declined payment card transaction authorization requests within the zone.

The zones may be defined by vertically extending lines intersecting the x-axis, defining time periods whose duration is predetermined or selectable, and by horizontally extending fraud score range stripes that intersect the y-axis of the graph. The time periods each extend back over a respective predetermined interval from a common starting point on the right-hand side of the x-axis, and thus overlap one another. In one example, during routine surveillance of the incoming individually scored payment card transaction authorization requests, the time periods may be set at certain fixed intervals. For example, the time periods could include six fixed intervals, which are fixed to look at time durations immediately previous to a particular time (e.g., the present time or a time associated with a payment card transaction authorization request), with lengths of 15 minutes, 1 hour, 6 hours, 24 hours, 7 days, and 28 days. During a suspected fraud attack, the duration of the time periods may be modified “on-the-fly” to provide data that the merchant profiling engine or machine learning algorithms need to fully ascertain parameters of the fraud attack. The fraud score stripes may also overlap one another.

The zones may each be an area of the graph defined by a particular time period and a particular fraud score range stripe, that is, by the intersection of one of the time periods and one of the fraud score range stripes. When the incoming payment card transaction authorization requests are plotted on the graph, each will be in at least one of the zones of the graph. All the instances of the payment card transaction authorization requests that are in each zone can be tallied together, yielding a single value representing how many payment card transaction authorization requests of a certain fraud score range arrived within a certain time period for the merchant, merchant bank, merchant category, or other grouping of merchants associated with the data represented on the graph. In addition, all of the amounts (e.g., monetary amounts) of the payment card transaction authorization requests that are in each zone can be totaled together, yielding a single value representing the total amount (e.g., the total dollar value) for the payment card transaction authorization requests within a certain fraud score range that arrived within a certain time period for the merchant, merchant bank, merchant category, or other grouping of merchants associated with the data represented on the graph. Moreover, all of the payment card transaction authorization requests that are in each zone and which have already been declined or rejected can be counted, yielding a single value representing how many declined payment card transaction authorization requests within a certain fraud score range arrived within a certain time period for the merchant, merchant bank, merchant category, or other grouping of merchants associated with the data represented on the graph. Additional or alternative cumulative metrics for the payment card transaction authorization requests in each zone may also be calculated.

Although the zones are described above as being defined graphically, in some embodiments the zones for each merchant, merchant bank, merchant category, or other grouping of merchants are defined and/or tracked by storing and parsing data structures in a computer memory for each merchant, merchant bank, merchant category, or other grouping of merchants, without graphically displaying the zones and scored authorization requests.

Ratios developed from each metric, such as the tallies, totals, and counts, for any selected two of the time periods in a given fraud score range stripe may reveal information that helps detect fraud. For example, a ratio of two tallies of payment card transaction authorization requests from the same stripe over different time periods reveals a change in payment card transaction authorization requests of similar fraud scores over the two time periods for the merchant, merchant bank, merchant category, or other grouping of merchants associated with the data represented on the graph. For another example, a ratio of two total values of amounts of payment card transaction authorization requests from the same stripe over different time periods reveals a change in the total amount for the payment card transaction authorization requests of similar fraud scores over the two time periods for the merchant, merchant bank, merchant category, or other grouping of merchants associated with the data represented on the graph. For another example, a ratio of two counts of declined payment card transaction authorization requests from the same stripe over different time periods reveals a change in the number of declined payment card transaction authorization requests of similar fraud scores over the two time periods for the merchant, merchant bank, merchant category, or other grouping of merchants associated with the data represented on the graph.

As used herein, “ratio striping value” may refer to any ratio of tallies, totals, counts, or other suitable metric across a fraud score range stripe over two time periods, such as the above. Ratio striping values may be a confirmation of a suspected fraud attack determined by, for example, an upstream fraud detection model, or may provide additional information for a second or subsequent payment card fraud analysis. Notably, because each time period extends back from a common point in time and the denominator time period extends back farther, the ratio striping values will always lie on a scale from 0 to 1, and thus are “pre-conditioned” to serve as useful inputs into a machine learning algorithm. For example, if a ratio striping value is taken for authorization requests preliminarily scored within a given fraud score range in the previous six hours as compared to over the previous twenty-four hours, any authorization requests for the fraud stripe in the previous six hour zone must also fall within the fraud stripe for the previous twenty-four hour zone, causing the ratio of the two values to fall on a scale from 0 to 1. The closer to a value of “1” the ratio is, the more likely it may be that a pattern of coordinated or otherwise related fraud attempts has begun. Moreover, tracking such an uptick in tallies, cumulative transaction amounts, and/or declines can detect fraud quickly even when the individually scored payment card transaction authorization requests are in a low-fraud-score stripe (e.g., when the type of fraud being perpetrated on on-line merchants in a particular category is one in which few indicia of fraud are present in the characteristics of the individual transactions taken separately).

The process of the merchant profiling engine is useful in at least two ways in the analysis of the incoming payment card transaction authorization requests. Once the merchant profiling described above is complete, the merchant profiling data may be used directly for trending and/or pattern recognition analysis to facilitate identifying a fraud attack. The results of the trending and pattern recognition analysis may be output directly to an operator dashboard or transmitted to downstream analysis components or a fraud management system located remotely from the fraud detection computer device. In addition, the ratios of the metrics for two zones may be used to generate feature inputs to a downstream fraud detection model, such as one that applies machine learning algorithms.

This fraud detection computer device and method increases the effectiveness of payment card fraud detection. First a fraud risk scoring model processes the incoming payment card transaction authorization requests to assess a preliminary fraud risk score. The scored payment card transaction authorization requests, or some scored subset of the payment card transaction authorization requests, is transmitted to the merchant profiling engine for additional processing. The output of the merchant profiling engine is forwarded on to a downstream fraud detection model where machine learning algorithms use the output of the merchant profiling engine to perform additional analyses of the merchant profiling engine output.

Further, in some embodiments, a notification system may be triggered by a combination of one or more threshold-based alerts (e.g., alerts indicating the presence of multiple real-time, non-correlated statistical anomalies). For example, the system may provide a visual, email, text message, or other notification to analysts when a change in fraudulent transaction velocity has increased over certain time periods.

The technical problems addressed by this system include at least one of: (i) undetected network-based fraud events on a payment card transaction network, especially those targeted at a particular merchant, merchant bank, merchant category, or other grouping of merchants; (ii) increased network load based on some types of fraud events; (iii) computational burdens imposed by automated fraud monitoring systems; and (iv) too little contrast between fraudulent transactions and legitimate transactions in some time frames to make detection possible. Other technical problems addressed by the system and methods described herein may include increased network usage (slowing down the network) due to undetected frauds (e.g., systematic attacks to determine card verification numbers through trial and error).

The methods and systems described herein may be implemented using computer programming or engineering techniques including computer software, firmware, hardware, or any combination or subset thereof, wherein the technical effects may be achieved by performing at least one of the following steps: (a) receiving a first plurality of payment card transaction authorization requests associated with a plurality of payment card transactions from merchants through the payment card network; (b) operating on the first plurality of payment card transaction authorization requests using a first type of algorithm in a first fraud detection model, (c) generating preliminary fraud scores for the plurality of payment card transaction authorization requests in the first fraud detection model; (d) tallying, for each merchant and/or grouping of merchants associated with the plurality of payment card transaction authorization requests, the scored payment card transaction authorization requests according to ranges of preliminary fraud scores and time periods of receipt of each payment card transaction authorization request; (e) determining, for each merchant and/or grouping of merchants associated with the plurality of payment card transaction authorization requests, the total values of the transaction amounts of the scored payment card transaction authorization requests within ranges of preliminary fraud scores and time periods of receipt of each payment card transaction authorization request; (f) counting, for each merchant and/or grouping of merchants associated with the plurality of payment card transaction authorization requests, the scored payment card transaction authorization requests within ranges of preliminary fraud scores and time periods of receipt that have been declined; (g) tracking the tallies, total values, decline counts, and/or similar metrics using data structures specific to a merchant, merchant bank, merchant category, or other grouping of merchants; (h) determining ratio striping values from the tallies, total values, counts and/or other metric; (i) displaying a graph of the scored payment card transaction authorization requests in a plurality of time periods and/or a plurality of fraud score range stripes, thereby enabling a user to visually analyze potentially fraudulent events in the payment card transaction network; (j) transmitting the ratio striping values and scored payment card transaction authorization requests to a second, or downstream, fraud detection model; (k) applying one or more selectable machine learning algorithms to the scored payment card transaction authorization requests using the ratio striping values and the selected machine learning algorithms; (l) calculating reweighted fraud scores for the scored payment card transaction authorization requests based on at least one of the preliminary fraud scores and the ratio striping values using the selected machine learning algorithms; (m) generating an approve or decline recommendation for a payment card transaction authorization request by the second fraud detection model based on at least one of a preliminary fraud score, a reweighted fraud score, and the ratio striping values; (n) generating a machine learning feedback signal and applying the machine learning feedback signal to the machine learning algorithms and/or the parameters of the merchant profiling engine.

The resulting technical effect achieved by this system is at least one of: (i) reducing network-based fraud events through early detection; (ii) reducing network-based fraud events through multiple fraud detection methods; (iii) applying both individual transaction fraud scoring and a cumulative fraud detection model to payment card authorization requests associated with a particular merchant, merchant bank, merchant category, or other grouping of merchants prior to forwarding of the authorization requests to an issuer; (iv) enabling visual network data views to detect fraud events; and (v) eliminating economic loss through, e.g., early detection and reaction to fraudulent network events. Thus, the system enables enhanced fraud detection on the payment card transaction network. Once a pattern of fraudulent activity is detected and identified, further fraudulent payment card transaction attempts may be reduced or isolated from further processing on the payment card interchange network, which results in a reduced amount of fraudulent network traffic and reduced processing time devoted to fraudulent transactions, and thus a reduced burden on the network.

As used herein, the term “database” may refer to either a body of data, a relational database management system (RDBMS), or to both. As used herein, a database may include any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object oriented databases, and any other structured collection of records or data that is stored in a computer system. The above examples are example only, and thus are not intended to limit in any way the definition and/or meaning of the term database. Examples of RDBMS's include, but are not limited to including, Oracle® Database, MySQL, IBM® DB2, Microsoft® SQL Server, Sybase®, and PostgreSQL. However, any database may be used that enables the systems and methods described herein. (Oracle is a registered trademark of Oracle Corporation, Redwood Shores, Calif.; IBM is a registered trademark of International Business Machines Corporation, Armonk, N.Y.; Microsoft is a registered trademark of Microsoft Corporation, Redmond, Wash.; and Sybase is a registered trademark of Sybase, Dublin, Calif.)

As used herein, a “processor” may include any programmable system including systems using central processing units, microprocessors, micro-controllers, reduced instruction set circuits (RISC), application specific integrated circuits (ASICs), logic circuits, and any other circuit or processor capable of executing the functions described herein. The above examples are example only, and are thus not intended to limit in any way the definition and/or meaning of the term “processor.”

As used herein, the terms “software” and “firmware” are interchangeable, and include any computer program stored in memory for execution by a processor, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are example only, and are thus not limiting as to the types of memory usable for storage of a computer program.

In one embodiment, a computer program is provided, and the program is embodied on a computer readable medium. In an example embodiment, the system is executed on a single computer system, without requiring a connection to a server computer. In a further embodiment, the system is being run in a Windows® environment (Windows is a registered trademark of Microsoft Corporation, Redmond, Wash.). In yet another embodiment, the system is run on a mainframe environment and a UNIX® server environment (UNIX is a registered trademark of X/Open Company Limited located in Reading, Berkshire, United Kingdom). The application is flexible and designed to run in various different environments without compromising any major functionality. In some embodiments, the system includes multiple components distributed among a plurality of computing devices. One or more components may be in the form of computer-executable instructions embodied in a computer-readable medium.

The systems and processes are not limited to the specific embodiments described herein. In addition, components of each system and each process can be practiced independent and separate from other components and processes described herein. Each component and process can also be used in combination with other assembly packages and processes.

As used herein, the terms “payment card,” “transaction card,” and “financial transaction card” refer to any suitable payment card, such as a credit card, a debit card, a prepaid card, a charge card, a membership card, a promotional card, a frequent flyer card, an identification card, a prepaid card, a gift card, and/or any other payment device that may hold payment account information, such as mobile phones, smartphones, personal digital assistants (PDAs), key fobs, and/or computers. Each type of payment device can be used as a method of payment for performing a transaction.

As used herein, the term “fraud” is used in the context of payment card transactions and refers, generally, to an unprivileged use of a payment card. For example, a thief may steal a consumer's payment card or information from that payment card (e.g., a payment account number [PAN], expiration date, security code) and attempt to use the payment card for purchases. This type of transaction may be monitored by, for example, a fraud detection system within a payment network. Further, as used herein, a “suspected fraudulent transaction” is a payment card transaction that is suspected to be fraudulent, but which has not yet been confirmed as fraudulent by, for example, the consumer of the underlying payment card, or the issuing bank, or an analyst associated with the fraud detection system.

As used herein, the term “real-time” is used, in some contexts, to refer to a regular updating of data within a system such as the fraud detection systems, the fraud management systems, and/or the displays described herein. When a system is described as processing or performing a particular operation “in real-time,” this may mean within seconds or minutes of an occurrence of some trigger event, such as new data being generated, or on some regular schedule, such as every minute. In other contexts, some payment card transactions require “real-time” fraud operations, such as fraud scoring, which refers to operations performed during authorization of a payment card transaction (i.e., between the moment that a new payment card transaction is initiated from, for example, a merchant, and the time that an authorization decision is made, for example, back to that merchant). In such a context, “near real-time” fraud operations are operations conducted shortly after the payment card transaction has occurred (i.e., after an authorization decision is made).

As used herein, transaction velocity generally relates to a number of qualifying transactions initiated by one or more consumers using one or more payment devices over a selected period of time, where the transactions qualify if they meet one or more qualifying criteria (e.g., having a fraud score within a certain fraud score range, being made at a particular merchant or group of merchants, being associated with a particular issuer or group of issuers).

The following detailed description illustrates embodiments of the disclosure by way of example and not by way of limitation. It is contemplated that the disclosure has general application to fraud management of payment card transactions.

As used herein, an element or step recited in the singular and proceeded with the word “a” or “an” should be understood as not excluding plural elements or steps, unless such exclusion is explicitly recited. Furthermore, references to “example embodiment” or “one embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.

FIG. 1 is a schematic block diagram of a fraud analysis computing system 100 for detecting fraudulent network events in a payment card interchange network, such as payment card interchange network 28 (shown in FIG. 3). For example, fraudsters may introduce fraudulent transactions through a merchant 24 in an attempt to deceive an issuer 30 into authorizing a transaction with a payment card that is not owned and/or controlled by the person presenting the payment card at a time of purchase. Such transactions may be monitored for fraudulent activity.

Fraudulent transactions may strain the processing and network resources of payment card interchange network 28. For example, some types of attempted fraud include a large number of attempted online transactions in a short period of time, which may limit a bandwidth of payment card interchange network 28 that is available for legitimate transactions. For another example, fraudulent transactions that are not detected prior to authorization by issuer 30 may result in additional activity over payment card interchange network 28 such as voids, rollbacks of cleared and settled transactions, and so forth, which may reduce processing speed and bandwidth available for legitimate transactions.

In the example embodiment, fraud analysis computing system 100 includes a first fraud detection model 102 communicatively coupled to a plurality of merchants 24 directly or through at least one merchant bank 26. A merchant profiling engine 104, including a processor 105, is communicatively coupled to first, or upstream, fraud detection model 102 and is configured to generate a plurality of data structures 106. Fraud analysis computing system 100 also includes a second, or downstream, fraud detection model 108 communicatively coupled to merchant profiling engine 104. In some embodiments, two or more of first fraud detection model 102, merchant profiling engine 104, and second fraud detection model 108 are implemented on a common computing platform. In alternative embodiments, each of first fraud detection model 102, merchant profiling engine 104, and second fraud detection model 108 are implemented on separate computing platforms and coupled together in electronic communication.

First fraud detection model 102 is configured to receive a plurality of payment card transaction authorization requests 110 from plurality of merchants 24 either directly or from the at least one merchant bank 26. In various embodiments, payment card transaction authorization requests 110 are received by payment card interchange network 28 (shown in FIG. 3) and forwarded to first fraud detection model 102. First fraud detection model 102 is configured to analyze each of the received plurality of payment card transaction authorization requests 110 on an individual basis (that is, without regard to characteristics of other incoming payment card transaction authorization requests) for fraud, and to assign a preliminary fraud score to each of the plurality of payment card transaction authorization requests 110. The preliminary fraud score is indicative of a likelihood of fraud being associated with a respective one of the payment card transaction authorization requests 110.

The scored payment card transaction authorization requests 112 are transmitted to merchant profiling engine 104. Merchant profiling engine 104 is configured to sort the scored payment card transaction authorization requests 112 by selected merchant groups. Each merchant group may be defined to include one or more merchants 24, merchant bank 26, all merchants in a selected merchant category, or another pre-selected grouping of merchants. It should be understood that a “merchant group” may be defined to include only a single merchant. Moreover, for each merchant 24, merchant bank 26, merchant category, or other grouping of merchants, merchant profiling engine 104 is configured to generate a respective data structure 106 that sorts the scored payment card transaction authorization requests 112 over a plurality of fraud score range stripes. Each of the fraud score range stripes ranges from an upper fraud score threshold to a lower fraud score threshold. In some embodiments, at least two of the fraud score range stripes overlap, such that a particular scored payment card transaction authorization request may be stored in two locations in the corresponding data structure 106.

Merchant profiling engine 104 is further configured to parse each data structure 106 over a plurality of time periods and calculate, for each of the time periods, at least one cumulative metric from the scored payment card transaction authorization requests 112. In addition, merchant profiling engine 104 is configured to determine, for each merchant group, a ratio of a first value of the metric in a first fraud score range stripe from a first time period with respect to a second value of the metric in the first fraud score range stripe during a second time period, wherein the second time period extends back farther in time than the first time period (i.e., a ratio striping value for the first and second time periods for the particular fraud score range stripe).

In the example embodiment, data structure 106 for each merchant 24, merchant bank 26, merchant category, or other grouping of merchants is parsed to determine a tally of each payment card transaction authorization request scored within each fraud score range stripe over each of the plurality of time periods. Merchant profiling engine 104 is also configured to determine a plurality of ratio striping values of a first tally in a first stripe from a first time period with respect to a second tally in the first stripe during a second time period.

In the example embodiment, data structure 106 for each merchant 24, merchant bank 26, merchant category, or other grouping of merchants also is parsed to determine a cumulative total of the transaction amounts of each payment card transaction authorization request scored within each fraud score range stripe over each of the plurality of time periods. Merchant profiling engine 104 is also configured to determine a plurality of ratio striping values of a first total in a first stripe from a first time period with respect to a second total in the first stripe during a second time period.

In the example embodiment, data structure 106 for each merchant 24, merchant bank 26, merchant category, or other grouping of merchants further is parsed to determine a count of the declined payment card transaction authorization requests scored within each fraud score range stripe over each of the plurality of time periods. For example, “declined” payment card transaction authorization requests are those declined or rejected by an issuing bank, such as issuer 30. Merchant profiling engine 104 is also configured to determine a plurality of ratio striping values of a first count in a first stripe from a first time period with respect to a second count in the first stripe during a second time period.

In the example embodiment, as large numbers of scored payment card transaction authorization requests 112 continue to be received by merchant profiling engine 104, the common starting point of the time periods used by merchant profiling engine 104 is updated to a more recent time in order to consider the most recent payment card transaction authorization requests in the fraud analysis. Due to the structure of data structures 106, merchant profiling engine 104 simply re-parses existing data structures 106 for each merchant 24, merchant bank 26, merchant category, or other grouping of merchants, rendering the ratio striping values derived therefrom amenable to rapid storage, calculation, and updating, enabling fraud detection by second fraud model 108 to be updated frequently, and in some embodiments in near real time. The use of data structures 106 thus provides an advantage over at least some known fraud detection systems. In some embodiments, older scored payment card transaction authorization requests 112 are correspondingly purged from data structure 106 as they age out of the longest time period 214 that merchant profiling engine 106 is configured to consider. In addition, the use of separate data structures 106 for each merchant 24, merchant bank 26, merchant category, or other grouping of merchants including each of the tally, total, and count as described above also reveal information that is particularly helpful in detecting fraud using second fraud model 108, as described below. In alternative embodiments, merchant profiling engine 104 is configured to parse data structure 106 for each merchant 24, merchant bank 26, merchant category, or other grouping of merchants to obtain any suitable combination of these or other metrics derived for payment card transaction authorization requests scored within each fraud score range stripe over each of the plurality of time periods, and merchant profiling engine 104 is also configured to determine a plurality of ratios of a first value of each metric in a first stripe from a first time period with respect to a second value in the first stripe during a second time period.

Merchant profiling engine 104 is further configured to provide feature inputs 114 for second fraud detection model 108 based on the determined plurality of ratio striping values. The set of feature inputs 114 are used to update or modify parameters of second fraud detection model 108 applied to scored payment card transaction authorization requests 112, facilitating the identification of potential occurrences of multiple related payment card transaction fraud attempts over payment card interchange network 28 (shown in FIG. 3). In some embodiments, second fraud detection model 108 includes a plurality of machine learning algorithms 124. In various embodiments, machine learning algorithms 124 may be selectable, either automatically or by an operator, and may include at least one of an Artificial Neural Network (ANN) machine learning algorithm and a Support Vector Machine (SVM) machine learning algorithm. Second fraud detection model 108 may be configured to execute multiple machine learning algorithms 124 singly or simultaneously in groups.

Feature inputs 114 affect the operation of second fraud detection model 108 by changing parameters of second fraud detection model 108 that are applied to scored payment card transaction authorization requests 112. In other words, operation of second fraud detection model 108 changes based on the generated set of feature inputs, that is, as the generated set of feature inputs 114 changes. For example, feature inputs 114 are used to train machine learning algorithms 124. In some embodiments, feature inputs 114 generated by merchant profiling engine 104 are used to adjust node weights applied by second fraud detection model 108 to external inputs (e.g., scored payment card transaction authorization requests 112) to, or internal signals (e.g., intra-node signals) within, the machine learning algorithm. Additionally or alternatively, feature inputs 114 are provided as input signals into machine learning algorithms 124. Second fraud detection model 108 is configured to perform at least one of: alerting merchant 24, merchant bank 26, merchant category, or other grouping of merchants associated with a particular data structure 106 to a potential ongoing coordinated fraud attempt; calculating reweighted fraud scores for the scored payment card transaction authorization requests based on at least one of the preliminary fraud scores and feature inputs 114, prior to forwarding the payment card transaction authorization requests to issuer 30; generating an approve or decline recommendation for a payment card transaction authorization request based on at least one of a preliminary fraud score, a reweighted fraud score, and feature inputs 114; flagging payment card transaction authorization requests 110 associated with merchant 24, merchant bank 26, merchant category, or other grouping of merchants associated with the particular data structure 106 associated with the potential ongoing coordinated fraud attempt for other special handling; generating a machine learning feedback signal 132; and applying machine learning feedback signal 132 to at least one of machine learning algorithms 124 and merchant profiling engine 104. In the example embodiment, after scored payment card transaction authorization requests 112 are processed by second fraud detection model 108, the corresponding payment card transaction authorization requests 110 are modified to include, for example, the reweighted fraud score and/or accept/decline recommendation generated by second fraud detection model 108, and routed to issuer 30 (e.g., via payment card interchange network 28 shown in FIG. 3) as re-scored payment card transaction authorization requests 119.

In some embodiments, the use of the ratio striping values to generate feature inputs 114 for second fraud detection model 108 further increases a processing speed of computing system 100. For example, the time periods used to define data structures 106 are selected as progressively longer time bands extending backward in time from a common starting point, such as the current time or the time stamp of a payment card transaction authorization request currently being processed, causing each of the ratio striping values as generated to lie between 0 and 1. Values ranging between 0 and 1 are easily conditioned to serve as feature inputs 114 (e.g., node weights) for machine learning algorithms 124, thus avoiding a need for time- and resource-consuming additional processing by merchant profiling engine 104 to generate feature inputs 114. In some embodiments, feature inputs 114 are set to equal the ratio striping values, such that the ratio striping values are provided directly to second fraud detection model 108. In other such embodiments, merchant profiling engine 104 provides limited additional conditioning of the ratio striping values to generate feature inputs 114, such as by squaring each of the ratio striping values to generate corresponding feature inputs 114. For example, the further conditioning, such as by squaring the values, facilitates increasing a stability of feature inputs 114, by reducing an effect of transient spikes in the ratio striping values on the value of the corresponding feature inputs 114. In alternative embodiments, feature inputs 114 are calculated from the ratio striping values in any suitable fashion.

In some embodiments, fraud analysis computing system 100 is configured to operate merchant profiling engine 104 over a first time segment using a first set of time periods and/or fraud stripe ranges to generate the plurality of ratio striping values, and in response to machine learning feedback signal 132 at the end of the first time segment, to select a second set of time periods and/or fraud stripe ranges to generate the plurality of ratio striping values going forward after the end of the first time segment. In alternative embodiments, merchant profiling engine 104 selects a different set of time periods and/or fraud stripe ranges in response to a signal originating from an operator of fraud analysis computing system 100 (e.g., based on the operator's viewing of graphical user interface 150), automatically from another component of fraud analysis computing system 100, or from an external system or component.

In some embodiments, fraud analysis computing system 100 is configured to operate second fraud detection model 108 using a first machine learning algorithm 124, and then in response to machine learning feedback signal 132, to automatically switch to operating second fraud detection model 108 using a second machine learning algorithm 124. In alternative embodiments, the signal causing second fraud detection model 108 to switch among machine learning algorithms 124 originates from an operator of fraud analysis computer system 100 (e.g., based on the operator's viewing of graphical user interface 150), automatically from another component of fraud analysis computing system 100, or from an external system or component.

In various embodiments, fraud analysis computing system 100 further includes a graphical user interface 150 configured to display information to a user in real time through a dashboard application 118. FIG. 2 illustrates graph 122 displayed on graphical user interface 150. For example, graphical user interface 150 is displayable on a display screen of a client system 414 (shown in FIG. 4). With reference to FIGS. 1 and 2, in the example embodiment, graph 122 includes an x-axis 202 graduated in units of time and a y-axis 204 graduated in units of fraud score. Typically, fraud scores are presented on a 0-100 or 0-1000 unit scale. Graph 122 displays horizontal fraud score range stripes 206, each delineated by an upper fraud score threshold 208 and a lower fraud score threshold 210.

Graph 122 also displays vertically extending time period boundaries 212 that intersect x-axis 202 and define a corresponding plurality of time periods 214. More specifically, each time period 214 is defined from a current analysis time 213 back to one of time period boundaries 212. In one example, during routine surveillance of the incoming scored payment card transaction authorization requests 112, time boundaries may be set at certain fixed intervals with respect to current analysis time 213. For example, time period boundaries 212 could define six fixed intervals, which are fixed to look at time durations immediately previous to current analysis time 213 (e.g., the present time or a timestamp associated with a payment card transaction authorization request most recently added to data structures 106), with lengths of 15 minutes, 1 hour, 6 hours, 24 hours, 1 days, and 28 days. During a suspected fraud attack a location of time period boundaries 212 may be modified “on-the-fly” to provide data that better enables merchant profiling engine 104 or machine learning algorithms 124, 126 to ascertain parameters of the fraud attack.

In the example embodiment, merchant profiling engine 104 provides for display on graph 122 each incoming scored payment card transaction authorization request 112 associated with merchant 24, merchant bank 26, merchant category, or other grouping of merchants, plotted by fraud score and time stamp. As time advances, new transactions are added at the right-hand side of graph 122, while older transactions scroll off of the left-hand side. Graph 122 thus provides a visual indication to a user of how the tally of payment card transaction authorization requests in each fraud score stripe 206 is changing over time. Moreover, in certain embodiments, a transaction amount associated with each plotted payment card transaction authorization request is represented proportionally by a size and/or color (e.g., ranging from blue or “cold” for smaller transaction amounts to red or “hot” for higher transaction amounts) of the symbol used on graph 122. Additionally or alternatively, graphical user interface 150 displays declined payment card transaction authorization requests on graph 122 using a different color for the symbol and/or a different type of symbol. In some embodiments, graphical user interface 150 enables the user to select among one or more metrics, and method of display of each metric, for display on graph 122. Thus, graphical user interface 150 provided by merchant profiling engine 104 and/or dashboard application 118 enables the user to draw inferences about patterns of fraudulent activity that may be occurring with respect to merchant 24, merchant bank 26, merchant category, or other grouping of merchants, even for payment card transaction authorization requests that have been scored individually as low fraud risks.

In alternative embodiments, merchant profiling engine 104 does not provide graph 122. Nevertheless, graph 122 provides a useful visual illustration of zones 216 for which cumulative metrics, based on information in the scored payment card transaction authorization requests in each fraud score stripe 206, are calculated by merchant profiling engine 104 as discussed above. More specifically, data regarding scored payment card transaction authorization requests 112 stored in data structures 106 for each merchant 24, merchant bank 26, merchant category, or other grouping of merchants is parsed over each time period 214 for each fraud score stripe 206, and the cumulative metrics are calculated for the respective zone 216.

For purposes of illustration, two zones 216 are illustrated in FIG. 2. A first zone 216 extends from current analysis time 213 back to a first time period boundary 215, a second zone 216 extends from current analysis time 213 back to an earlier second time period boundary 217, and both zones are bounded within a particular fraud stripe 207 of the plurality of fraud stripes 206. For example, but not by way of limitation, first time period boundary 215 defines a backward-looking time interval of six hours and second time period boundary 217 defines a backward-looking time interval of twenty-four hours. Merchant profiling engine 104 parses data structure 106 for payment card transaction authorization requests scored within fraud stripe 207 and time stamped between current analysis time 213 and first time period boundary 215. In the example embodiment, data structure 106 includes payment card transaction authorization requests pre-sorted into fraud stripes 206, enabling the time parsing process for the first and second illustrated zones 216 to operate solely on transactions within fraud stripe 207, thereby increasing a speed of the parsing process, which advantageously enables merchant profiling engine 104 to continuously update the metrics for each zone 216 as time moves forward and the time stamps of payment card transaction authorization requests in each data structure 106 are correspondingly shifted across time period boundaries 212.

In the example embodiment, merchant profiling engine 104 calculates the tally, total amount, and declined count of the identified payment card transaction authorization requests and associates these metrics with the first zone 216. Similarly, merchant profiling engine 104 parses the portion of data structure 106 that includes payment card transaction authorization requests scored within fraud stripe 207 to identify payment card transaction authorization requests that are time stamped between current analysis time 213 and second time period boundary 217. Merchant profiling engine 104 calculates the tally, total amount, and declined count of the identified payment card transaction authorization requests and associates these metrics with the second zone 216. In the example embodiment, merchant profiling engine 104 further calculates the ratio striping values associated with fraud stripe 207, the first zone 216, and the second zone 216 as the ratio of the tally (i.e., number of transactions) in the first zone 216 to the tally in the second zone 216, the ratio of the total amount of transactions in the first zone 216 to the total amount of transactions in the second zone 216, and the ratio of the count of declined transactions in the first zone 216 to the count of declined transactions in the second zone 216. Merchant profiling engine 104 may perform similar operations for each pair of time periods 214 within fraud stripe 207, and for each pair of the plurality of time periods 214 for other fraud stripes 206. It should again be noted that the speed advantages provided by sorting scored payment card transaction authorization requests 112 into data structures 106 corresponding to each merchant 24, merchant bank 26, merchant category, or other grouping of merchants, and in some embodiments by further sorting the payment card transaction authorization requests in each data structure 106 by fraud stripe 206, enable merchant profiling engine 104 to perform these operations in near real time for the extremely large number of payment card transaction authorization requests 110 that are processed by payment card interchange network 28.

One measurement of potential fraudulent activity directly uses the ratio striping values based on tallies, total values, or decline counts of payment card transaction authorization requests from the same fraud stripe 206 over a pair of time periods 214. For example, a ratio of the tallies from the first zone 216 to the second zone 216 reveals a change in payment card transaction authorization requests of similar fraud scores between the two time periods for the merchant 24, merchant bank 26, merchant category, or other grouping of merchants under consideration. As another example, a ratio of the total transaction amounts from the first zone 216 to the second zone 216 reveals a change in the total value of the amounts for payment card transaction authorization requests of similar fraud scores between the two time periods for the merchant 24, merchant bank 26, merchant category, or other grouping of merchants. As yet another example, a ratio of the decline counts from the first zone 216 to the second zone 216 reveals a change in declined payment card transaction authorization requests of similar fraud scores between the two time periods for the merchant 24, merchant bank 26, merchant category, or other grouping of merchants. The merchant profiling data as embodied in the ratio striping values is useful in at least two ways. The ratio striping values by themselves may demonstrate trending and/or patterns that facilitate identifying a fraud attack or confirming a suspected fraud attack previously determined by, for example, an upstream fraud detection model. The results of the trending and pattern recognition analysis may be output directly to graphical user interface 150 or transmitted to downstream analysis components or a fraud management system located remotely from the fraud detection computer device. Additionally or alternatively, the ratio striping values may provide the basis for inputs into a second or subsequent payment card fraud analysis, and are particularly well-suited to serve as inputs into machine learning algorithms, as described above with respect to second fraud model 108. In some embodiments, second fraud model 108 learns to detect underlying relationships between actual fraud events and ratio striping values associated with various zones 216 that may be difficult to detect by a human operator.

In some embodiments, as noted above, fraud analysis computer system 100 is implemented as part of, or in association with, a payment card interchange network 28. FIG. 3 is a schematic diagram illustrating an example multi-party payment card industry system 20 for enabling ordinary payment-by-card transactions in which merchants 24 and issuer banks 30 do not need to have a one-to-one special relationship. Embodiments described herein may relate to a payment card system, such as a credit card payment system using the MasterCard® interchange network. The MasterCard® interchange network is a set of proprietary communications standards promulgated by MasterCard International Incorporated® for the exchange of financial transaction data and the settlement of funds between financial institutions that are members of MasterCard International Incorporated®. (MasterCard is a registered trademark of MasterCard International Incorporated located in Purchase, N.Y.).

In a typical payment card system, a financial institution called the “issuer” issues a payment card, such as a credit card, to a consumer or cardholder 22, who uses the payment card to tender payment for a purchase from merchant 24. To accept payment with the payment card, merchant 24 must normally establish an account with a financial institution that is part of the financial payment system. This financial institution is usually called the “merchant bank,” the “acquiring bank,” or the “acquirer.” When cardholder 22 tenders payment for a purchase with a payment card, merchant 24 requests authorization from an acquirer or merchant bank 26 for the amount of the purchase. The request may be performed over the telephone, but is usually performed through the use of a point-of-sale terminal, which reads cardholder's 22 account information from a magnetic stripe, a chip, or embossed characters on the payment card and communicates electronically with the transaction processing computers of merchant bank 26. Alternatively, merchant bank 26 may authorize a third party to perform transaction processing on its behalf. In this case, the point-of-sale terminal will be configured to communicate with the third party. Such a third party is usually called a “merchant processor,” an “acquiring processor,” or a “third party processor.”

Using payment card interchange network 28, computers of merchant bank 26 or merchant processor will communicate with computers of issuer bank 30 by sending a payment card transaction authorization request. Based on the payment card transaction authorization request, issuer 30 determines whether cardholder's 22 account 32 is in good standing and whether the purchase is covered by cardholder's 22 available credit line. Based on these determinations, the request for authorization will be declined or accepted by issuer 30. If the request is accepted, an authorization code is issued to merchant 24.

When a request for authorization is accepted, the available credit line of cardholder's 22 account 32 is decreased. Normally, a charge for a payment card transaction is not posted immediately to cardholder's 22 account 32 because bankcard associations, such as MasterCard International Incorporated®, have promulgated rules that do not allow merchant 24 to charge, or “capture,” a transaction until goods are shipped or services are delivered. However, with respect to at least some debit card transactions, a charge may be posted at the time of the transaction. When merchant 24 ships or delivers the goods or services, merchant 24 captures the transaction by, for example, appropriate data entry procedures on the point-of-sale terminal. This may include bundling of approved transactions daily for standard retail purchases. If cardholder 22 cancels a transaction before it is captured, a “void” is generated. If cardholder 22 returns goods after the transaction has been captured, a “credit” is generated. Payment card interchange network 28 and/or issuer bank 30 stores the payment card information, such as a type of merchant, amount of purchase, date of purchase, in a database 420 (shown in FIG. 4).

After a purchase has been made, a clearing process occurs to transfer additional transaction data related to the purchase among the parties to the transaction, such as merchant bank 26, payment card interchange network 28, and issuer bank 30. More specifically, during and/or after the clearing process, additional data, such as a time of purchase, a merchant name, a type of merchant, purchase information, cardholder account information, a type of transaction, itinerary information, information regarding the purchased item and/or service, and/or other suitable information, is associated with a transaction and transmitted between parties to the transaction as transaction data, and may be stored by any of the parties to the transaction.

After a transaction is authorized and cleared, the transaction is settled among merchant 24, merchant bank 26, and issuer bank 30. Settlement refers to the transfer of financial data or funds among merchant's 24 account, merchant bank 26, and issuer bank 30 related to the transaction. Usually, transactions are captured and accumulated into a “batch,” which is settled as a group. More specifically, a transaction is typically settled between issuer bank 30 and payment card interchange network 28, and then between payment card interchange network 28 and merchant bank 26, and then between merchant bank 26 and merchant 24.

In the example embodiment, payment card interchange network 28 routes payment card transaction authorization request through fraud analysis computing system 100 as described above. Detection of patterns of fraudulent activity may enable payment card interchange network 28 to identify and prevent fraudulent transactions prior to authorization by issuer 30, thereby improving transaction processing speed and bandwidth available for legitimate transactions. Fraud analysis computing system 100 may be configured to provide fraud data associated with payment card transactions to a downstream fraud management system (not shown) for further processing. Fraud analysis computing system 100 may be incorporated on one or more computing devices within payment card interchange network 28, or may be embodied in one or more separate components communicatively accessible to payment card interchange network 28.

FIG. 4 is a simplified block diagram of an example fraud analysis computing system 100 in communication with payment interchange network 28 in accordance with one embodiment of the present disclosure. In the example embodiment, fraud analysis computing system 100 is implemented on a server system 412. A plurality of client systems 414 is connected to server system 412. In one embodiment, client systems 414 are computers including a web browser, such that server system 412 is accessible to client systems 414 using the Internet. Client systems 414 are interconnected to the Internet through network connections 415, such as a local area network (LAN) or a wide area network (WAN), dial-in-connections, cable modems, special high-speed Integrated Services Digital Network (ISDN) lines, and RDT networks. Client systems 414 could be any device capable of connecting to the Internet including a web-based phone, PDA, or other web-based connectable equipment.

Server system 412 includes a database server 416 connected to a database 420, which contains information on a variety of matters, as described below in greater detail. In one embodiment, database 420 is centralized on, for example, server system 412 and can be accessed by potential users at one of client systems 414 by logging onto server system 412 through one of client systems 414. In an alternative embodiment, database 420 is stored remotely from server system 412 and may be non-centralized.

Database 420 may include a single database having separated sections or partitions, or may include multiple databases, each being separate from each other. Database 420 may store transaction data generated over payment card interchange network 28 including data relating to payment card transactions, fraudulent payment card transactions, and fraud scoring values and rules. Database 420 may also store account data for a plurality of cardholders, including at least one of a cardholder name, a cardholder address, an account number, other account identifiers, and transaction information. Database 420 may also store merchant data including a merchant identifier that identifies each merchant registered to use the network, and instructions for settling transactions including merchant bank account information. Database 420 may also store purchase data associated with items being purchased by a cardholder from a merchant, and authorization request data. Database 420 may also store fraud information received from fraud analysis computing system 100.

In the example embodiment, one of client systems 414 is a user computer device associated with a user of fraud analysis computing system 100. For example, the user computer device is configured to display graphical user interface 150 (shown in FIGS. 1 and 2) generated by fraud analysis computing system 100 via a web browser or dashboard application 118 (shown in FIG. 1) installed on the user computer device. Web browsers enable users of client system 414 to display and interact with media and other information typically embedded on a web page or a website associated with server system 412. Dashboard application 118 allows users to interact with a server application on server system 412.

Others of client systems 414 may be associated with acquirer or merchant bank 26 and issuer 30 (shown in FIG. 3). In addition, client systems 414 may include a computer system associated with at least one of an online bank, a bill payment outsourcer, an acquirer bank, an acquirer processor, an issuer bank associated with a payment card, an issuer processor, a remote payment system, customers and/or billers. In the example embodiment, server system 412 is associated with payment card interchange network 28, and may be referred to as an interchange computer system. Server system 412 may be used for general processing of payment card transaction data as well as analyzing fraud data associated with payment card transactions.

FIG. 5 illustrates an example configuration of one of client systems 414 operated by a user 501, such as an analyst. In the example embodiment, client system 414 includes a processor 505 for executing instructions. In some embodiments, executable instructions are stored in a memory area 510. Processor 505 may include one or more processing units, for example, a multi-core configuration. Memory area 510 is any device allowing information such as executable instructions and/or written works to be stored and retrieved. Memory area 510 may include one or more computer readable media.

Client system 414 also includes at least one media output component 515 for presenting information to user 501. Media output component 515 is any component capable of conveying information to user 501. For example, media output component is configured to display graphical user interface 150 (shown in FIG. 1) to user 501. In some embodiments, media output component 515 includes an output adapter such as a video adapter and/or an audio adapter. An output adapter is operatively coupled to processor 505 and operatively coupleable to an output device such as a display device, a liquid crystal display (LCD), organic light emitting diode (OLED) display, or “electronic ink” display, or an audio output device, a speaker or headphones.

In some embodiments, client system 414 includes an input device 520 for receiving input from user 501. Input device 520 may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel, a touch pad, a touch screen, a gyroscope, an accelerometer, a position detector, or an audio input device. A single component such as a touch screen may function as both an output device of media output component 515 and input device 520. Client system 414 may also include a communication interface 525, which is communicatively coupleable to a remote device such as server system 412. Communication interface 525 may include, for example, a wired or wireless network adapter or a wireless data transceiver for use with a mobile phone network, Global System for Mobile communications (GSM), 3G, or other mobile data network or Worldwide Interoperability for Microwave Access (WIMAX).

FIG. 6 illustrates an example configuration of server system 412. Server system 412 includes a processor 605 for executing instructions. Instructions may be stored in a memory area 610, for example. Processor 605 may include one or more processing units (e.g., in a multi-core configuration) for executing instructions. The instructions may be executed within a variety of different operating systems on the server system 412, such as UNIX, LINUX, Microsoft Windows®, etc. It should also be appreciated that upon initiation of a computer-based method, various instructions may be executed during initialization. Some operations may be required in order to perform one or more processes described herein, while other operations may be more general and/or specific to a particular programming language (e.g., C, C #, C++, Java, or other suitable programming languages, etc.).

Processor 605 is operatively coupled to a communication interface 615 such that server system 412 is capable of communicating with remote devices such as client systems 414 (shown in FIG. 4) or another server system 412. For example, communication interface 615 may receive requests from client system 414 via the Internet, as illustrated in FIG. 4.

Processor 605 may also be operatively coupled to a storage device 634, which may be used to implement database 420. Storage device 634 is any computer-operated hardware suitable for storing and/or retrieving data. In some embodiments, storage device 634 is integrated in server system 412. For example, server system 412 may include one or more hard disk drives as storage device 634. In other embodiments, storage device 634 is external to server system 412 and may be accessed by a plurality of server systems 412. For example, storage device 634 may include multiple storage units such as hard disks or solid state disks in a redundant array of inexpensive disks (RAID) configuration. Storage device 634 may include a storage area network (SAN) and/or a network attached storage (NAS) system.

In some embodiments, processor 605 is operatively coupled to storage device 634 via a storage interface 620. Storage interface 620 is any component capable of providing processor 605 with access to storage device 634. Storage interface 620 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any component providing processor 605 with access to storage device 634.

Memory area 610 may include, but is not limited to, random access memory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and non-volatile RAM (NVRAM). The above memory types are exemplary only, and are thus not limiting as to the types of memory usable for storage of a computer program.

In operation, fraud analysis computing system 100 (shown in FIG. 1) runs on server system 412. In some embodiments, at least one of first fraud model 102, merchant profiling engine 104, and second fraud model 108 runs on the same server system 412. Alternatively, each of first fraud model 102, merchant profiling engine 104, and second fraud model 108 runs on separate server systems 412 communicatively coupled to each other. User 501 (shown in FIG. 5) interacts with server system 412, and with processes such as merchant profiling engine 104, using one of client systems 414 (shown in FIG. 4).

FIG. 7 shows an example configuration of fraud analysis computing system 100. Database 420 is coupled to several separate components within fraud analysis computing system 100, which perform specific tasks. In the example embodiment, server system 412, database server 416, and database 420 are all contained in a single computing device. In other embodiments, fraud management server system 412, database server 416, and database 420 may be contained in separate computing devices which are communicatively coupled to each other.

Fraud analysis computing system 100 in the example embodiment includes an information collecting component 702 for collecting information from users into database 420, a scored payment card transaction authorization request receiving component 704 for receiving scored payment card transaction authorization requests 112 from first fraud detection model 102 and originating from a plurality of merchants 24, a data structure generating component 706 to generate data structures 106 for corresponding merchant groups, each having scored payment card transaction authorization requests sorted by fraud score, a data structure parsing component 708 to parse the data structures over a plurality of time periods, and a cumulative metric calculating component 710 to calculate cumulative metrics for various ones of the time periods based on the parsed data structures. Fraud analysis computing system 100 further includes a ratio striping value determining component 712 for determining ratio striping values from the cumulative metrics as described above. A feature input generating component 714 generates sets of feature inputs 114 using the determined ratio striping values. A downstream fraud detection model component 716 receives the sets of feature inputs 114 and applies a second fraud detection model to scored payment card transaction authorization requests 112, wherein parameters of the second fraud detection model are configured to change based on the received feature inputs 114, as discussed above.

Fraud analysis computing system 100 also includes a database communication component 718 that includes a query component 720 programmed to receive a specific query from client system 414, and an access component 722 to access database 420. Query component 720 is programmed for receiving a specific query, a data request and/or a data message (collectively referred to as a “query”) from one of a plurality of users. Database communication component 718 searches and processes received queries against storage device 634 containing a variety of information collected by collection component 702. In an exemplary embodiment, database 420 is divided into a plurality of sections, including but not limited to, a Transaction and Purchase Data Section 724, a Merchant Data Section 726, and a Cardholder Account Data Section 728. These sections within database 420 are interconnected to update and retrieve the information as required.

FIG. 8 is a flow diagram of a computer-implemented merchant profiling method 800 for detecting fraudulent network transactions in a payment card transaction network. Method 800 uses at least one computing device, such as fraud analysis computing system 100. The at least one computing device has at least one processor, such as processor 105, and the at least one processor performs the steps of the method.

With reference also to FIGS. 1 and 2, method 800 includes receiving 802 a plurality of scored payment card transaction authorization requests 112 originating from a plurality of merchants 24. For example, scored payment card transaction authorization requests 112 are received via first fraud detection model 102. Each of the scored payment card transaction authorization requests 112 is associated with a respective preliminary fraud score. Method 800 also includes generating 804, for each of a plurality of merchant groups of the plurality of merchants 24, a respective data structure 106. The data structure sorts the scored payment card transaction authorization requests 112 associated with the respective merchant group over a plurality of fraud score range stripes 206 based on the corresponding preliminary fraud score. Method 800 includes parsing 806 each of the data structures over a plurality of time periods 214. Each of the time periods 214 extends back over a respective predetermined interval from a common starting point (such as current analysis time 213). In some embodiments, the common starting point is a time associated with one of the scored payment card transaction authorization requests 112 (e.g. current analysis time 213). In some embodiments, the plurality of time periods 214 includes the predetermined intervals of six hours and twenty-four hours.

Method 800 further includes calculating 808, for each merchant group for each of the time periods, at least one cumulative metric from the scored payment card transaction authorization requests associated with each of the fraud score range stripes. In some embodiments, the at least one cumulative metric includes a tally of each scored payment card transaction authorization request 112 scored within each fraud score range stripe 206 over each of the plurality of time periods 214, a cumulative total of transaction amounts of scored payment card transaction authorization requests 112 scored within each fraud score range stripe 206 over each of the plurality of time periods 214, and a count of declined scored payment card transaction authorization requests 112 scored within each fraud score range stripe 206 over each of the plurality of time periods 214.

Method 800 also includes determining 810, for each merchant group, a plurality of ratio striping values. Each of the ratio striping values is a ratio of a first value of the at least one cumulative metric in a first of the fraud score range stripes from a first time period with respect to a second value of the at least one cumulative metric in the first fraud score range stripe during a second time period, wherein the second time period extends back farther in time than the first time period. Method 800 includes also generating 812 a set of feature inputs 114 using the determined plurality of ratio striping values. In some embodiments, the feature inputs are generated to be equal to the determined plurality of ratio striping values. Alternatively, the feature inputs 114 are generated by conditioning the ratio striping values, such as by squaring each ratio striping value to obtain a corresponding feature input. Method 800 further includes applying 814 a second fraud detection model to the scored payment card transaction authorization requests 112. Parameters of the second fraud detection model are configured to change based on the generated set of feature inputs. In some embodiments, the set of generated feature inputs is applied to train the plurality of machine learning algorithms operating within a downstream fraud detection model.

Method 800 optionally includes determining a potential fraud attack associated with a merchant 24 based on the plurality of ratio striping values and outputting a potential fraud attack alert. Additionally or alternatively, method 800 optionally includes calculating a reweighted fraud score for each of the scored payment card transaction authorization requests 112 based on the corresponding preliminary fraud score and the generated set of feature inputs 114. In some such embodiments, method 800 further includes generating re-scored payment card transaction authorization requests each including at least one of (i) the respective reweighted fraud score and (ii) a recommendation regarding whether to approve or decline the respective scored payment card transaction authorization request 112 based on at least one of the preliminary fraud score, the reweighted fraud score, and the determined plurality of ratio striping values.

Method 800 optionally includes generating a graphical user interface 150 including a graph 122 having an x-axis 202 graduated in units of time and a y-axis 204 graduated in units of fraud score. The graph 122 shows the plurality of fraud score range stripes 206 extending horizontally. Each fraud score range stripe 206 is delineated by an upper fraud score threshold 208 and a lower fraud score threshold 210. Graph 122 shows vertically extending time period boundaries 212 intersecting x-axis 202. Graphical user interface 150 is displayable on a display screen of a user computer device 414.

As used herein, “machine learning” refers to statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed for that specific task.

As will be appreciated based on the foregoing specification, the above-discussed embodiments of the disclosure may be implemented using computer programming or engineering techniques including computer software, firmware, hardware or any combination or subset thereof. Any such resulting program, having computer-readable and/or computer-executable instructions, may be embodied or provided within one or more computer-readable media, thereby making a computer program product, i.e., an article of manufacture, according to the discussed embodiments of the disclosure. The computer readable media may be, for instance, a fixed (hard) drive, diskette, optical disk, magnetic tape, semiconductor memory such as read-only memory (ROM) or flash memory, etc., or any transmitting/receiving medium such as the Internet or other communication network or link. The article of manufacture containing the computer code may be made and/or used by executing the instructions directly from one medium, by copying the code from one medium to another medium, or by transmitting the code over a network.

As used herein, the term “non-transitory computer-readable media” is intended to be representative of any tangible computer-based device implemented in any method or technology for short-term and long-term storage of information, such as, computer-readable instructions, data structures, program modules and sub-modules, or other data in any device. Therefore, the methods described herein may be encoded as executable instructions embodied in a tangible, non-transitory, computer readable medium, including, without limitation, a storage device and/or a memory device. Such instructions, when executed by a processor, cause the processor to perform at least a portion of the methods described herein. Moreover, as used herein, the term “non-transitory computer-readable media” includes all tangible, computer-readable media, including, without limitation, non-transitory computer storage devices, including, without limitation, volatile and nonvolatile media, and removable and non-removable media such as firmware, physical and virtual storage, CD-ROMs, DVDs, and any other digital source such as a network or the Internet, as well as yet to be developed digital means, with the sole exception being a transitory, propagating signal.

As used herein, the term “computer” and related terms, e.g., “computing device”, are not limited to integrated circuits referred to in the art as a computer, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller (PLC), an application specific integrated circuit, and other programmable circuits, and these terms are used interchangeably herein.

As used herein, the term “cloud computing” and related terms, e.g., “cloud computing devices” refers to a computer architecture allowing for the use of multiple heterogeneous computing devices for data storage, retrieval, and processing. The heterogeneous computing devices may use a common network or a plurality of networks so that some computing devices are in networked communication with one another over a common network but not all computing devices. In other words, a plurality of networks may be used in order to facilitate the communication between and coordination of all computing devices.

As used herein, the term “mobile computing device” refers to any computing device which is used in a portable manner including, without limitation, smart phones, personal digital assistants (“PDAs”), computer tablets, hybrid phone/computer tablets (“phablet”), or other similar mobile device capable of functioning in the systems described herein. In some examples, mobile computing devices may include a variety of peripherals and accessories including, without limitation, microphones, speakers, keyboards, touchscreens, gyroscopes, accelerometers, and metrological devices. Also, as used herein, “portable computing device” and “mobile computing device” may be used interchangeably.

Approximating language, as used herein throughout the specification and claims, may be applied to modify any quantitative representation that could permissibly vary without resulting in a change in the basic function to which it is related. Accordingly, a value modified by a term or terms, such as “about” and “substantially”, are not to be limited to the precise value specified. In at least some instances, the approximating language may correspond to the precision of an instrument for measuring the value. Here and throughout the specification and claims, range limitations may be combined and/or interchanged, such ranges are identified and include all the sub-ranges contained therein unless context or language indicates otherwise.

This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims. 

The invention claimed is:
 1. A computing system for detecting fraudulent network events in a payment card network, said computing system comprising at least one processor and a memory storing instructions executable by the at least one processor to execute a merchant profiling engine and a downstream fraud detection model, wherein: the merchant profiling engine is configured to: receive a plurality of scored payment card transaction authorization requests originating from a plurality of merchants, wherein each of the scored payment card transaction authorization requests is associated with a respective preliminary fraud score that is proportional to indicia of fraud present in an individual transaction corresponding to the respective scored payment card transaction authorization request; generate, for each of a plurality of merchant groups of the plurality of merchants, a respective data structure, wherein the data structure sorts the scored payment card transaction authorization requests associated with the respective merchant group over a plurality of fraud score range stripes, each of the fraud score range stripes defined by an upper fraud score threshold and a lower fraud score threshold and including one or more of the scored payment card transaction authorization requests having a value of the corresponding preliminary fraud score therebetween; parse each of the data structures over a plurality of time periods, wherein each of the time periods extends back over a respective predetermined interval from a common starting point; calculate, for each merchant group for each of the time periods, at least one cumulative metric from the one or more scored payment card transaction authorization requests associated with each of the fraud score range stripes; determine, for each merchant group, a plurality of ratio striping values, each of the ratio striping values being a ratio of a first value of the at least one cumulative metric in a first of the fraud score range stripes from a first time period with respect to a second value of the at least one cumulative metric in the first fraud score range stripe from a second time period, wherein the second time period extends back farther in time than the first time period resulting in each of the plurality of ratio striping values being constrained to have values between zero and one; and generate a set of feature inputs using the determined plurality of ratio striping values, wherein each of the set of feature inputs is derived from a corresponding one of the plurality of ratio striping values; and the downstream fraud detection model is configured to: apply a machine learning model to the scored payment card transaction authorization requests; adjust node weights of the machine learning model based on the generated set of feature inputs; transmit a machine learning feedback signal to the merchant profiling engine, wherein the machine learning feedback signal causes the merchant profiling engine to modify at least one of a duration associated with the plurality of time periods and the upper and lower thresholds associated with the plurality of fraud score range stripes; identify, via the application of the machine learning model, an ongoing occurrence of multiple related payment card transaction fraud attempts over the payment card network corresponding to at least one of the plurality of merchant groups; and output an alert regarding the ongoing occurrence to the at least one merchant group.
 2. The computing system of claim 1, wherein the merchant profiling engine is further configured to generate a graphical user interface including a graph having an x-axis graduated in units of time and a y-axis graduated in units of fraud score, the graph showing the plurality of fraud score range stripes extending horizontally, each delineated by an upper fraud score threshold and a lower fraud score threshold, the graph showing vertically extending time period boundaries intersecting the x-axis, the graphical user interface displayable on a display screen of a user computer device.
 3. The computing system of claim 1, wherein the downstream fraud detection model is further configured to: determine a potential fraud attack associated with a merchant based on the plurality of ratio striping values; and output a potential fraud attack alert.
 4. The computing system of claim 1, wherein the common starting point is a time associated with one of the scored payment card transaction authorization requests, and wherein the plurality of time periods includes the predetermined intervals of six hours and twenty-four hours.
 5. The computing system of claim 1, wherein the downstream fraud detection model comprises a plurality of machine learning algorithms configured to execute singly or simultaneously in groups, and wherein the generated set of feature inputs is applied to train the plurality of machine learning algorithms operating within the downstream fraud detection model.
 6. The computing system of claim 1, wherein the merchant profiling engine is configured to calculate the at least one cumulative metric including a tally of each scored payment card transaction authorization request scored within each fraud score range stripe over each of the plurality of time periods.
 7. The computing system of claim 1, wherein the merchant profiling engine is configured to calculate the at least one cumulative metric including a cumulative total of transaction amounts of scored payment card transaction authorization requests scored within each fraud score range stripe over each of the plurality of time periods.
 8. The computing system of claim 1, wherein the merchant profiling engine is configured to calculate the at least one cumulative metric including a count of declined scored payment card transaction authorization requests scored within each fraud score range stripe over each of the plurality of time periods.
 9. The computing system of claim 1, wherein the merchant profiling engine is configured to generate the set of feature inputs as being equal to the determined plurality of ratio striping values.
 10. The computing system of claim 1, wherein the downstream fraud detection model is further configured to calculate a reweighted fraud score for each of the scored payment card transaction authorization requests based on the corresponding preliminary fraud score and the generated set of feature inputs.
 11. The computing system of claim 10, wherein the downstream fraud detection model is further configured to generate re-scored payment card transaction authorization requests each including at least one of (i) the respective reweighted fraud score and (ii) a recommendation regarding whether to approve or decline the respective scored payment card transaction authorization request based on at least one of the corresponding preliminary fraud score, the reweighted fraud score, and the determined plurality of ratio striping values.
 12. A computer-implemented merchant profiling method for detecting fraudulent network transactions in a payment card transaction network, said method using at least one computing device having at least one processor and a memory storing instructions executable by the at least one processor to execute a merchant profiling engine and a downstream fraud detection model, said method comprising: receiving, by the at least one processor, a plurality of scored payment card transaction authorization requests originating from a plurality of merchants, wherein each of the scored payment card transaction authorization requests is associated with a respective preliminary fraud score that is proportional to indicia of fraud present in an individual transaction corresponding to the respective scored payment card transaction authorization request; generating, by the at least one processor for each of a plurality of merchant groups of the plurality of merchants, a respective data structure, wherein the data structure sorts the scored payment card transaction authorization requests associated with the respective merchant group over a plurality of fraud score range stripes, each of the fraud score range stripes defined by an upper fraud score threshold and a lower fraud score threshold and including one or more of the scored payment card transaction authorization requests having a value of the corresponding preliminary fraud score therebetween; parsing, by the at least one processor, each of the data structures over a plurality of time periods, wherein each of the time periods extends back over a respective predetermined interval from a common starting point; calculating, by the at least one processor for each merchant group for each of the time periods, at least one cumulative metric from the one or more scored payment card transaction authorization requests associated with each of the fraud score range stripes; determining, by the at least one processor for each merchant group, a plurality of ratio striping values, each of the ratio striping values being a ratio of a first value of the at least one cumulative metric in a first of the fraud score range stripes from a first time period with respect to a second value of the at least one cumulative metric in the first fraud score range stripe from a second time period, wherein the second time period extends back farther in time than the first time period resulting in each of the plurality of ratio striping values being constrained to have values between zero and one; generating, by the at least one processor, a set of feature inputs using the determined plurality of ratio striping values, wherein each of the set of feature inputs is derived from a corresponding one of the plurality of ratio striping values; and applying, by the at least one processor, a machine learning model to the scored payment card transaction authorization requests; adjusting, by the at least one processor, node weights of the machine learning model based on the generated set of feature inputs; and transmitting, by the at least one processor, a machine learning feedback signal to the merchant profiling engine, wherein the machine learning feedback signal causes the merchant profiling engine to modify at least one of a duration associated with the plurality of time periods and the upper and lower thresholds associated with the plurality of fraud score range stripes; identifying, via the application of the machine learning model, an ongoing occurrence of multiple related payment card transaction fraud attempts over the payment card network corresponding to at least one of the plurality of merchant groups; and outputting an alert regarding the ongoing occurrence to the at least one merchant group.
 13. The computer-implemented method of claim 12, further comprising the step of generating a graphical user interface including a graph having an x-axis graduated in units of time and a y-axis graduated in units of fraud score, the graph showing the plurality of fraud score range stripes extending horizontally, each delineated by an upper fraud score threshold and a lower fraud score threshold, the graph showing vertically extending time period boundaries intersecting the x-axis, the graphical user interface displayable on a display screen of a user computer device.
 14. The computer-implemented method of claim 12, wherein the machine learning model comprises a plurality of machine learning algorithms configured to execute singly or simultaneously in groups, and wherein the generated set of feature inputs is applied to train the plurality of machine learning algorithms.
 15. The computer-implemented method of claim 12, further comprising generating, by the at least one processor, re-scored payment card transaction authorization requests each including at least one of (i) a respective reweighted fraud score calculated by the machine learning model and (ii) a recommendation regarding whether to approve or decline the respective scored payment card transaction authorization request based on at least one of the corresponding preliminary fraud score, the reweighted fraud score, and the determined plurality of ratio striping values.
 16. At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon wherein, when executed by at least one processor, the computer-executable instructions cause the at least one processor to execute a merchant profiling engine and a downstream fraud detection model, and wherein the computer-executable instructions further cause the at least one processor to: receive a plurality of scored payment card transaction authorization requests originating from a plurality of merchants, wherein each of the scored payment card transaction authorization requests is associated with a respective preliminary fraud score; generate, for each of a plurality of merchant groups of the plurality of merchants, a respective data structure, wherein the data structure sorts the scored payment card transaction authorization requests associated with the respective merchant group over a plurality of fraud score range stripes, each of the fraud score range stripes defined by an upper fraud score threshold and a lower fraud score threshold and including one or more of the scored payment card transaction authorization requests having a value of the corresponding preliminary fraud score therebetween; parse each of the data structures over a plurality of time periods, wherein each of the time periods extends back over a respective predetermined interval from a common starting point; calculate, for each merchant group for each of the time periods, at least one cumulative metric from the one or more scored payment card transaction authorization requests associated with each of the fraud score range stripes; determine, for each merchant group, a plurality of ratio striping values, each of the ratio striping values being a ratio of a first value of the at least one cumulative metric in a first of the fraud score range stripes from a first time period with respect to a second value of the at least one cumulative metric in the first fraud score range stripe from a second time period, wherein the second time period extends back farther in time than the first time period resulting in each of the plurality of ratio striping values being constrained to have values between zero and one; generate a set of feature inputs using the determined plurality of ratio striping values, wherein each of the set of feature inputs is derived from a corresponding one of the plurality of ratio striping values; apply a machine learning model to the scored payment card transaction authorization requests; adjust node weights of the machine learning model based on the generated set of feature inputs; transmit a machine learning feedback signal to the merchant profiling engine, wherein the machine learning feedback signal causes the merchant profiling engine to modify at least one of a duration associated with the plurality of time periods and the upper and lower thresholds associated with the plurality of fraud score range stripes; identify, via the application of the machine learning model, an ongoing occurrence of multiple related payment card transaction fraud attempts over the payment card network corresponding to at least one of the plurality of merchant groups; and output an alert regarding the ongoing occurrence to the at least one merchant group.
 17. The computer-readable storage media of claim 16, wherein the computer-executable instructions further cause the processor to calculate the at least one cumulative metric including a tally of each scored payment card transaction authorization request scored within each fraud score range stripe over each of the plurality of time periods.
 18. The computer-readable storage media of claim 16, wherein the computer-executable instructions further cause the processor to calculate the at least one cumulative metric including a cumulative total of transaction amounts of scored payment card transaction authorization requests scored within each fraud score range stripe over each of the plurality of time periods.
 19. The computer-readable storage media of claim 16, wherein the computer-executable instructions further cause the processor to calculate the at least one cumulative metric including a count of declined scored payment card transaction authorization requests scored within each fraud score range stripe over each of the plurality of time periods. 